Combined Multiscale Exposures to Urban Greenspaces: A Spatially Explicit Approach
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| Publicado en: | PQDT - Global (2020) |
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ProQuest Dissertations & Theses
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| 045 | 2 | |b d20200101 |b d20201231 | |
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| 100 | 1 | |a Labib, S. M. | |
| 245 | 1 | |a Combined Multiscale Exposures to Urban Greenspaces: A Spatially Explicit Approach | |
| 260 | |b ProQuest Dissertations & Theses |c 2020 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a A steadily increasing number of studies have indicated the multidimensional relationships between exposure to greenspace and human health. These previous studies applied diverse, spatially explicit methods and datasets when measuring and modelling greenspace exposure. However, they often did not consider the key underlying principles of geospatial science, such as spatial scale. Potential uncertainties regarding the estimation of greenspace exposure and modelling relationships between greenspace and human health are inherent in such studies. Therefore, research to investigate the spatial dimensions (e.g., scale, data, methods) of exposure to greenspace is warranted. Previous studies measuring greenspace exposure have usually only considered individual exposure types (i.e., availability, accessibility, and visibility). Consequently, there are no existing methodological approaches that standardise and harmonise these three individual metrics to produce a composite estimate of aggregate greenspace exposure. Additional research is essential to develop metrics for greenspace exposure that can reduce the multiplicity of current heterogeneous approaches to greenspace exposure estimation. The four papers foundational to this thesis encompass different methodological approaches and used diverse spatial data to address existing challenges in greenspace exposure assessment. Greater Manchester in the UK was used as the case study area. However, the methods are intended to be transferable to other urban areas.The first paper provides a systematic review of the spatial dimensions considered in previous studies evaluating the associations between greenspace and human health. These preceding studies used a wide range of spatial data, scales of analysis, and buffer distances, resulting in the likelihood that their results were vulnerable to scale effects. Furthermore, these studies represented a heterogeneous collection of study designs. Such heterogeneity of study design may have caused ambiguities in understanding the associations between greenspace and health amongst existing studies. There is a need for new methodological solutions to overcome the existing challenges.The second paper investigated the scale effects inherent in three commonly used metrics of greenspace availability exposure (i.e., NDVI, leaf area index, and land use land cover). Applying lacunarity analysis, this paper identifies the scale sensitivity and the upper limit of the acceptable scale range beyond which these greenspace metrics are vulnerable to scale effects. Using lacunarity weightings, this study also provides a new methodological solution for measuring greenspace availability exposure that combines multiple, properly weighted metrics to reduce the scaling effect. This novel metric offers a unique solution for measuring greenspace availability at high resolutions, independent of scale.The third paper provides an innovative methodological approach to modelling eye-level greenspace visibility using a fusion of digital elevation and land use-land cover data. This study applied a classic viewshed analysis for visibility assessment to more than 86 million observer locations to achieve high resolution and granularity in eye-level exposure assessment. This paper also explores the differences between eye-level and top-down exposure metrics, and the results showed that these two exposure measures are complementary but distinct from one another.The fourth paper develops and evaluates a new methodological approach to measuring greenspaces accessibility at a high spatial resolution. This novel methodological approach allows the availability, accessibility, and visibility metrics of greenspace exposure to be combined to produce a composite greenspace exposure index (CGEI). | |
| 653 | |a Disease | ||
| 653 | |a Geographic information science | ||
| 653 | |a Brownfields | ||
| 653 | |a Outdoor air quality | ||
| 653 | |a Ecosystems | ||
| 653 | |a Industrial Revolution | ||
| 653 | |a Climate change | ||
| 653 | |a Global positioning systems--GPS | ||
| 653 | |a Nature | ||
| 653 | |a Vegetation | ||
| 653 | |a Health care | ||
| 653 | |a Public health | ||
| 653 | |a Land use | ||
| 653 | |a Urban areas | ||
| 653 | |a Mental health | ||
| 653 | |a Spatial data | ||
| 653 | |a Neighborhoods | ||
| 653 | |a Area planning & development | ||
| 653 | |a Atmospheric sciences | ||
| 653 | |a European history | ||
| 653 | |a History | ||
| 653 | |a Information science | ||
| 653 | |a Land use planning | ||
| 653 | |a Public policy | ||
| 653 | |a Academic disciplines | ||
| 773 | 0 | |t PQDT - Global |g (2020) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2700375611/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2700375611/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |